Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	136,990
2	South Dakota	134,737
3	Rhode Island	132,659
4	Utah	121,327
5	Tennessee	118,135
6	Arizona	116,355
7	Iowa	112,661
8	Oklahoma	111,799
9	Wisconsin	110,429
10	Nebraska	110,335
11	Arkansas	109,915
12	South Carolina	108,686
13	New Jersey	106,089
14	Alabama	105,628
15	Kansas	105,069
16	Indiana	103,700
17	Mississippi	103,103
18	Idaho	102,375
19	Illinois	100,453
20	Delaware	100,028
21	Nevada	99,450
22	New York	99,320
23	Montana	99,069
24	Georgia	98,510
25	Wyoming	98,144
26	Florida	97,993
27	Kentucky	97,343
28	Texas	97,257
29	Louisiana	96,391
30	Missouri	96,153
31	Minnesota	94,910
32	Massachusetts	94,735
33	California	93,393
34	New Mexico	92,161
35	Connecticut	89,692
36	North Carolina	88,947
37	Ohio	88,424
38	Alaska	87,503
39	Pennsylvania	83,156
40	Colorado	82,766
41	West Virginia	80,808
42	Michigan	80,482
43	Virginia	73,936
44	Maryland	69,803
45	District of Columbia	64,467
46	New Hampshire	64,308
47	Washington	49,295
48	Puerto Rico	45,519
49	Oregon	40,020
50	Maine	39,425
51	Vermont	33,123
52	Hawaii	21,663

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	804
2	New Jersey	461
3	Minnesota	445
4	Rhode Island	428
5	New York	364
6	Pennsylvania	362
7	Massachusetts	326
8	New Hampshire	322
9	Florida	300
10	Connecticut	292
11	Colorado	285
12	North Dakota	284
13	Delaware	283
14	Illinois	278
15	South Dakota	256
16	Maine	250
17	Alaska	240
18	Vermont	214
19	Nebraska	206
20	South Carolina	202
21	West Virginia	202
22	Maryland	200
23	Oklahoma	199
24	Virginia	191
25	Montana	190
26	Ohio	190
27	Idaho	184
28	Wisconsin	183
29	District of Columbia	182
30	Tennessee	182
31	Puerto Rico	181
32	Iowa	171
33	Indiana	159
34	Wyoming	157
35	Kentucky	152
36	North Carolina	147
37	Georgia	146
38	Utah	145
39	Texas	144
40	Oregon	124
41	Washington	120
42	Missouri	115
43	New Mexico	103
44	Nevada	102
45	Louisiana	98
46	Arizona	91
47	Mississippi	77
48	Kansas	69
49	Alabama	67
50	Arkansas	66
51	California	60
52	Hawaii	57

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,790
2	New York	2,590
3	Massachusetts	2,519
4	Rhode Island	2,488
5	Mississippi	2,379
6	Arizona	2,343
7	Connecticut	2,227
8	Louisiana	2,194
9	South Dakota	2,191
10	Alabama	2,177
11	Pennsylvania	1,982
12	North Dakota	1,967
13	Indiana	1,947
14	New Mexico	1,896
15	Arkansas	1,876
16	Illinois	1,873
17	Iowa	1,851
18	South Carolina	1,794
19	Georgia	1,768
20	Michigan	1,746
21	Tennessee	1,741
22	Nevada	1,724
23	Texas	1,695
24	Kansas	1,692
25	Oklahoma	1,685
26	Delaware	1,610
27	Ohio	1,603
28	Florida	1,578
29	District of Columbia	1,526
30	West Virginia	1,526
31	California	1,520
32	Missouri	1,466
33	Kentucky	1,413
34	Montana	1,399
35	Maryland	1,391
36	Wisconsin	1,265
37	Minnesota	1,241
38	Virginia	1,222
39	Wyoming	1,211
40	Nebraska	1,192
41	North Carolina	1,167
42	Idaho	1,114
43	Colorado	1,076
44	New Hampshire	920
45	Washington	704
46	Puerto Rico	671
47	Utah	670
48	Oregon	580
49	Maine	556
50	Alaska	408
51	Vermont	368
52	Hawaii	329

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Oklahoma	144
2	Montana	9
3	West Virginia	7
4	Michigan	5
5	New Jersey	5
6	Georgia	4
7	Nevada	4
8	Florida	3
9	Kentucky	3
10	Mississippi	3
11	Nebraska	3
12	New Mexico	3
13	New York	3
14	Oregon	3
15	Pennsylvania	3
16	Tennessee	3
17	Texas	3
18	Alabama	2
19	Arizona	2
20	California	2
21	District of Columbia	2
22	Idaho	2
23	Iowa	2
24	Louisiana	2
25	Maryland	2
26	Minnesota	2
27	Ohio	2
28	Rhode Island	2
29	South Carolina	2
30	Virginia	2
31	Arkansas	1
32	Connecticut	1
33	Illinois	1
34	Indiana	1
35	Massachusetts	1
36	North Carolina	1
37	Puerto Rico	1
38	Utah	1
39	Washington	1
40	Wisconsin	1
41	Alaska	0
42	Colorado	0
43	Delaware	0
44	Hawaii	0
45	Kansas	0
46	Maine	0
47	Missouri	0
48	New Hampshire	0
49	North Dakota	0
50	South Dakota	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	353,737	1	99
Chattahoochee	Georgia	336,389	2	99
Bent	Colorado	266,272	3	99
Dewey	South Dakota	244,908	4	99
Lincoln	Arkansas	244,702	5	99
Davidson	Tennessee	137,578	191	93
Richland	South Carolina	106,814	1017	67
York	South Carolina	103,663	1171	62
Orange	California	84,197	2075	33
Pierce	Washington	48,634	2898	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,515	1785	43
Davidson	Tennessee	1,321	2026	35
York	South Carolina	1,306	2039	35
Richland	South Carolina	1,277	2070	34
Pierce	Washington	682	2727	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.